AI Deals Are Stalling Because Nobody Owns the Outcome.
AI deals are not stalling because the models are “not good enough”. They stall because the commercial exchange is unclear. The moment AI does the work, the buyer is no longer purchasing a tool. They are purchasing a result. If the result is uncertain, and accountability is spread across procurement, legal, IT, Ops, and the vendor, the deal slows down exactly where money and risk get signed off.
You can see this in the buying process. A sales team demos capability. The customer asks for proof of impact, liability boundaries, operational ownership, and failure handling. If the vendor cannot answer those crisply, the only rational lever the buyer has left is to push price down as a proxy for transferring risk. Discounting is not a pricing strategy. It is a symptom of missing accountability.
In 2026, vendors that keep pricing AI like software will get squeezed. Seat based, feature based, and even usage based pricing fail to answer the buyer’s core question: what outcome will I get, and who owns it if it does not happen? Buyers will pay for value. They will not pay for ambiguity.
The real reason AI deals stall: unclear value and diluted accountability
Price objections usually mask a value clarity failure. Price is a trade off against perceived value and perceived risk. When value is vague, the “trade off” becomes simple. The buyer demands a lower price because risk is high and nobody is accountable.
When AI produces work, the customer expects you to own the outcome, not just provide a licence. That expectation shows up as friction in places that have nothing to do with product quality:
Security reviews get longer because the buyer is effectively delegating action to a system, not just storing data.
Pilots multiply because stakeholders want evidence that outcomes are repeatable, not just that the model can generate output.
Procurement terms shift to “prove it” clauses, service credits, and expanded liability language.
Expansions stall because the first deployment never established a measurement baseline, so nobody can defend the next budget request.
A familiar buying committee dynamic looks like this: the CFO asks for ROI and payback period; legal asks who is liable when the AI is wrong; Ops asks who will run it day to day; IT asks about governance and access. The vendor answers with features, tokens, and architecture diagrams. The mismatch is commercial, not technical.
When vendors cannot quantify impact or stand behind performance, discounting becomes a substitute for risk management. That is why “we can do 30 percent off if you sign this quarter” shows up so quickly. It is not because margins are fat. It is because accountability is thin.
“Usage based” is not “value based” and buyers know the difference
Usage based pricing is often presented as modern and fair. In practice, it can still leave the buyer holding the bag. They pay for activity, not results.
AI increases output. Output is not inherently valuable. Sending more emails does not guarantee more revenue. Handling more tickets does not guarantee higher CSAT or lower churn. Creating more leads does not guarantee pipeline quality. If the spend line item scales with volume while value remains uncertain, usage based pricing feels like paying to experiment.
Put it in concrete terms:
Per 1,000 actions in customer support: the buyer pays for every generated reply, triage, or classification. If those actions do not reduce handle time, deflect contacts, or improve resolution quality, the buyer has paid for motion.
Per resolution: the buyer pays when the customer’s problem is actually solved. That maps to operational metrics support leaders already run, and it is legible to finance.
This is where the adoption versus ownership gap, as David Elkington describes it, becomes commercially decisive. Adoption says, “teams are using it”. Ownership says, “someone is on the hook for the business result.” Usage based pricing can drive adoption while still failing ownership, because it does not force anyone to define and defend outcomes. When ownership is missing, deals stall at renewal, expansion, or CFO scrutiny.
Stripe is a useful analogy outside AI. Stripe’s pricing works because it is tied to a business event the buyer cares about: transactions processed. It is not “usage” in the abstract. It is value linked to revenue flow. AI vendors who price “per message” or “per action” often miss this. The buyer cannot connect that unit to a budget owner’s KPI.
Outcomes pricing works when the outcome is measurable, controllable, and defensible
Outcome pricing is not a slogan. It is an operational commitment. It works when the outcome metric is observable, attributable, and aligned to someone who actually owns budget.
That is why outcome framing in support is easier to buy than vague “productivity uplift”. Zendesk, Intercom and eDesk style messaging around resolutions and deflection resonates because it matches how support leaders already measure performance: time to resolution, backlog, contact rate, first contact resolution. “AI productivity” is a promise. “Pay per resolved case” is a commercial mechanism.
Outcome pricing forces discipline:
Define success criteria upfront, including baselines and measurement windows.
Instrument the workflow so results can be attributed, not debated in QBR theatre.
Share risk through guarantees, credits, or earn outs that reduce the buyer’s fear of paying for uncertainty.
The hard constraint is control. The vendor must control enough of the workflow to influence the result. If the outcome depends on five upstream systems, inconsistent data, and a customer process nobody follows, outcome pricing becomes gambling. The right answer is not to avoid outcomes. It is to pick outcomes you can actually defend, and to be explicit about shared responsibilities.
The operating model changes you need to make outcomes pricing viable
Outcomes pricing breaks immediately if your operating model is still built for licences.
Sales: comp plans cannot reward bookings alone. They must reward retention and realised value. Otherwise, sales will sell outcomes the delivery team cannot reliably produce.
Delivery and CS: implementation becomes performance engineering, not onboarding. The job is to move a metric, not to configure a dashboard.
Product and data: instrumentation becomes a product requirement. If you cannot prove impact in the customer’s language, you cannot price outcomes with confidence.
Governance: CIO.com’s “controlled autonomy” idea matters here. Enterprises will allow faster deployment when autonomy is paired with clear guardrails, auditability, and accountability. The goal is not compliance theatre. It is risk ownership at speed.
Contracts: define success, define exclusions, define what happens when reality deviates.
A practical mechanism is a one page value scorecard used in QBRs: baseline metric, target metric, realised movement, economic value, and price paid. Not NPS. Not “adoption”. A P and L relevant scorecard.
A workable guarantee structure looks like: baseline measured over 30 days; target improvement over the next 60; exclusions for broken upstream processes or missing access; and a make good mechanism such as service credits or reduced variable fees until performance returns.
A practical 2026 playbook: start with “what is this worth?” then package backwards
Start with economics, not packaging. Quantify upside in terms the buyer’s exec team already manages: revenue gain, cost removed, risk reduced, time to cash. Then pick one or two primary outcome metrics with a named executive owner. Examples that tend to work because they map to budgets:
Net new qualified meetings booked (sales leadership)
Renewal saved or churn prevented (revenue leadership)
Resolutions completed within SLA (support leadership)
Days sales outstanding reduced (finance leadership)
Package backwards into tiers that reflect accountability:
Assistive (tool): priced like software, for teams that want control and are willing to own results.
Guided (shared responsibility): shared measurement, shared optimisation cadence, partial variable pricing.
Owned outcome (vendor accountable): primary pricing unit is the outcome, with caps and floors.
A worked sketch: £120 per net new qualified meeting booked, with a quarterly cap tied to territory capacity and a floor commitment that funds onboarding and instrumentation. Or £400 per renewal saved, where “saved” is contractually defined (for example, renewal executed within a defined window, excluding accounts with unresolved service breaches).
The strategic point is learning velocity. Imperfect AI deployed early, with measurement and governance, compounds advantage faster than “perfect AI” deployed late. The winners in 2026 will not be the vendors with the best demos. They will be the vendors who can identify value, measure it cleanly, expose it to budget owners, and capture it through pricing that matches accountability.
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